TL;DR
What SQL Do Data Science Interviews Actually Test?
title: "SQL Query Cheatsheet for Data Scientist Interviews (Downloadable PDF)"
slug: "sql-query-cheatsheet-for-data-scientist-interview-template"
segment: "jobs"
lang: "en"
keyword: "SQL Query Cheatsheet for Data Scientist Interviews (Downloadable PDF)"
company: ""
school: ""
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date: "2026-06-18"
source: "factory-v2"
SQL Query Cheatsheet for Data Scientist Interviews (Downloadable PDF)
The candidates who drill the most LeetCode SQL problems often crash in live interviews because they never learned what hiring managers actually watch for. At a Meta Data Science HC in Q2 2023, we passed a candidate who missed two syntax details but correctly identified that a revenue query double-counted subscription renewals due to a missing window frame, while we rejected a candidate with perfect execution who couldn't explain why their 23-table join would cost $4,200 per run in Snowflake.
The gap is not query fluency. It is judgment under ambiguity.
What SQL Do Data Science Interviews Actually Test?
You will face three tiers, and most candidates overprepare for the wrong one.
Tier one: extraction and filtering. This is the "write a query to find users who signed up in the last 30 days and made a purchase" level. At a Stripe Data Science loop in 2024, this consumed exactly four minutes of a 45-minute session. The interviewer, a senior DS on the Payments Risk team, used it as a warm-up to calibrate communication style. Candidates who spent ten minutes optimizing this basic query signaled insecurity or lack of familiarity with real data scale.
Tier two: aggregation with context. This is where interviews live.
A Google Search DS interviewer asked: "Write a query to compute click-through rate by region, but exclude regions with fewer than 1000 impressions, and handle the case where a user clicks multiple times on the same ad." The successful candidate immediately asked three clarifying questions: how to define "region" (IP geolocation vs.
account country), whether to deduplicate clicks by session or by user, and whether to compute CTR as clicks/impressions or unique clicks/unique impressions. The rejected candidate wrote a technically correct query but built on assumptions that produced a 14% variance from the business metric.
Tier three: optimization and trade-off communication. At an Uber Eats Data Science debrief in Q4 2023, the hiring manager rejected a candidate whose query ran in 12 seconds but required four CTEs and a self-join, when a simpler subquery approach ran in 8 seconds with half the complexity. The problem was not the slower runtime. It was the candidate's inability to articulate when to prefer readability over performance, or how they would validate query correctness at Uber's scale of 800 million monthly orders.
The counter-intuitive truth: interviewers at Apple, Netflix, and Airbnb do not hire for SQL memory. They hire for the ability to construct a query while simultaneously defending choices about data quality, metric definition, and computational cost.
What Join and Window Function Questions Look Like in Practice?
Window functions are the single most predictive signal of seniority, and most candidates demonstrate only partial mastery.
At a Netflix Content Analytics interview in 2023, the question was: "For each user, find their first three viewing sessions, then compute the average time between session start and first content play." The candidate who received an offer used ROW_NUMBER() in a CTE to identify sessions, then LAG() to compute inter-session intervals, but the critical moment came when they proactively discussed: "I'm assuming session boundary is 30 minutes of inactivity, but I'd validate this with the product team since content type affects natural pause length." This demonstrated metric ownership, not just function fluency.
The rejected candidate used DENSERANK() instead of ROWNUMBER(), which produced duplicate ranks when sessions had identical timestamps, and defended the choice by saying "they're basically the same." They are not. DENSE_RANK() leaves no gaps, which breaks first-N filtering. The Netflix HC noted this as a "red flag on attention to edge cases in production data."
Join questions test not syntax but cardinality awareness. A DoorDash DS interviewer asked: "We have orders, deliveries, and refunds tables.
Compute refund rate by restaurant, but handle orders with multiple delivery attempts." The successful candidate immediately drew the relationship: orders to deliveries is one-to-many, deliveries to refunds is one-to-zero-or-one, and proposed a two-step aggregation to avoid fan-out. The rejected candidate wrote a single query with a three-way join, producing inflated refund counts by a factor of 2.3x, and only discovered the error when the interviewer asked them to sanity-check against total revenue.
The "not X, but Y" principle: it is not about memorizing window function syntax, but about recognizing when a business metric requires ordered logic versus set logic. RANK() for competition scenarios, ROW_NUMBER() for deduplication, LEAD()/LAG() for time-series transitions, and windowed aggregates for rolling metrics each encode different assumptions about data meaning.
> đź“– Related: Deep Dive: Google's AI PM Interview Questions with an RLAIF Focus
How Do You Handle Missing Data and Outliers in SQL?
This separates candidates who write queries from candidates who protect business decisions.
At a Lyft Data Science loop in 2022, the question involved computing driver earnings with missing trip distance values. The candidate who advanced to on-site immediately asked: "What does missing mean here? GPS failure, trip cancellation, or data pipeline delay?" They proposed COALESCE() with a driver-reported estimate only after confirming the missing mechanism, noting that imputing the mean would systematically undercount airport trips. The rejected candidate filled NULLs with zero "to be conservative," which the hiring manager noted would have triggered a driver payment dispute in production.
Outlier handling tests judgment about metric robustness versus completeness. A Palantir interviewer asked: "Compute average transaction value, but handle the top 1% of values." The strong candidate proposed two metrics: a raw average and a truncated mean (excluding top 1%), then discussed how to present both to stakeholders. The weak candidate automatically applied a filter without considering that the top 1% might represent legitimate enterprise customers, not data errors.
The specific technique that signals seniority: explicit NULL handling with business justification, not blanket ISNULL() or COALESCE(). At an Airbnb Data Science debrief, the HC specifically praised a candidate who wrote:
"For missing review scores, I'd use COALESCE(reviewscore, propertyavg_score) only if the property has 10+ reviews; otherwise NULL, because sparse imputation introduces noise that our ranking model amplifies."
This demonstrated understanding of imputation bias, not just syntax.
What Query Optimization Knowledge Do Interviewers Expect?
Less than you think, but what they do expect must be precise.
At an Amazon Alexa Shopping DS interview in 2023, the candidate was asked to optimize a slow query on a 2-billion-row table. The successful candidate identified three specific techniques: partition pruning on event_date, replacing a correlated subquery with a JOIN, and pushing predicates into a CTE to limit early materialization. They then stated: "I'd verify with EXPLAIN ANALYTICS in Redshift, but my priority is correctness before performance unless the SLA is breached."
The rejected candidate proposed indexing every column. In Amazon's Redshift environment, this revealed fundamental misunderstanding of columnar storage and sort keys. The hiring manager's debrief note: "Would create more problems than they solve."
The optimization framework used internally at Google and Meta follows a hierarchy: correct result, then bounded runtime, then resource cost, then maintainability. Candidates who jump to indexing or partitioning without establishing correctness first signal premature optimization. The specific phrase that advances candidates: "Let me first verify the query returns the right answer on a sample, then measure where time is going."
Execution plan reading is rarely tested directly, but verbalizing the approach to diagnosis is. A strong response at a Databricks DS loop: "I'd run EXPLAIN, look for table scans where I expect partition elimination, check if my JOIN order matches cardinality (smallest table first in a hash join), and verify I'm not spilling to disk due to memory limits."
> đź“– Related: Airbnb Growth PM Interview Questions 2026: Complete Guide
Preparation Checklist
- Master three tiered query types: warm-up extraction, business-metric aggregation with clarification, and optimization with trade-off defense. Practice verbalizing your thinking for the middle tier, not just typing solutions.
- Build a personal library of 8-10 window function patterns with business context: sessionization, cohort retention, funnel conversion, and running totals. Know which function encodes which assumption.
- Practice NULL handling with explicit business justification, not syntactic defaults. For each technique, know when it introduces bias and how to communicate that to stakeholders.
- Work through a structured preparation system (the PM Interview Playbook covers SQL interview frameworks with real debrief examples from Meta and Google loops, including how hiring committees weight technical correctness against communication clarity).
- Record yourself explaining queries aloud for 10 minutes without visual aids. The habit of verbalizing table relationships, edge cases, and metric definitions under time pressure is a separate skill from written fluency.
- Study one columnar data warehouse execution model deeply (Redshift, Snowflake, or BigQuery). Know how it differs from row-oriented databases and why that changes optimization strategy.
Mistakes to Avoid
BAD: Writing the most efficient query possible without validatingzejÄ…c
verification against sample data. At a Square DS interview, a candidate produced a 6-second query that undercounted merchant transactions by 18% due to an inner join that excluded test accounts. They spent 20 minutes optimizing the wrong result.
GOOD: Explicitly stating "I'll run this on last week's data first and compare against the dashboard total before optimizing." This builds trust and catches logic errors early.
BAD: Using subqueries when CTEs improve readability, or CTEs when simple joins suffice, without being able to defend the choice. A candidate at Shopify was asked "why three CTEs?" and answered "it's cleaner," then stumbled when asked about materialization overhead in their specific warehouse.
GOOD: Matching structure to query complexity and verbally flagging trade-offs: "I'm using a CTE here because the sessionization logic is complex and reused twice; for a one-time simple filter, I'd inline it."
BAD: Ignoring the interviewer when they hint at an error. At an Instacart loop, the interviewer said "are you sure about that join condition?" The candidate doubled down, missing that orders.orderdate and deliveries.deliverydate were in different timezones. The debrief noted "poor signal-to-noise ratio in collaboration."
GOOD: Treating interviewer engagement as a design partner, not an exam proctor. The phrase "That's a good catch—let me adjust" costs nothing and signals professional maturity.
FAQ
What SQL dialect should I prepare for data science interviews?
Prepare standard ANSI SQL with one dialect depth. Most FAANG and late-stage companies use BigQuery, Snowflake, Redshift, or Spark SQL; interviewers rarely test syntax that differs dramatically across these.
The exception is date functions and window frame syntax, which vary enough to cause hesitation. Know your target company's stack from job postings or recruiter confirmation, but prioritize logic portability. A 2023 Meta DS interviewer explicitly told a candidate: "I don't care if you write DATE_DIFF or DATEDIFF; I care if you recognize that my question about 'last 7 days' requires handling timezone and partial days."
How much of my preparation should focus on LeetCode-style SQL versus real business scenarios?
Not more than 30% on isolated LeetCode problems, which test algorithmic thinking in a sanitized environment. The remaining 70% should involve writing queries where the metric definition is ambiguous, the data has quality issues, and the output must be validated against a business sanity check. At Google, the DS interview rubric explicitly weights "handles ambiguity" and "communicates trade-offs" above "query executes correctly." A candidate at Waymo passed with a query containing a minor syntax error that they caught and fixed during discussion, because their metric reasoning was sound.
Should I prepare to write SQL on a whiteboard, in a document, or in a live coding environment?
Prepare for all three, as the modality affects your performance. Whiteboard SQL at Amazon and Meta requires verbalizing structure before writing; live coding in CoderPad or HackerRank at Stripe and Airbnb tests comfort with execution and debugging.
The specific failure mode: candidates who type quickly in live environments but cannot explain their logic without the code in front of them. At a Netflix debrief, the HC rejected a candidate who wrote elegant SQL but could not walk through it line-by-line when the screen was shared with a non-technical stakeholder. Practice explaining your query to someone who does not know the schema, without looking at your code.amazon.com/dp/B0GWWJQ2S3).